Fine Tuning

Methods

Domain Types

Dpo Hyperparameters

• dpo_hyperparameters: object { batch_size, beta, learning_rate_multiplier, n_epochs }

  The hyperparameters used for the DPO fine-tuning job.

  • batch_size: optional "auto" or number

    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

    • union_member_0: "auto"

    • union_member_1: number

  • beta: optional "auto" or number

    The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

    • union_member_0: "auto"

    • union_member_1: number

  • learning_rate_multiplier: optional "auto" or number

    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

    • union_member_0: "auto"

    • union_member_1: number

  • n_epochs: optional "auto" or number

    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

    • union_member_0: "auto"

    • union_member_1: number

Dpo Method

• dpo_method: object { hyperparameters }

  Configuration for the DPO fine-tuning method.

  • hyperparameters: optional object { batch_size, beta, learning_rate_multiplier, n_epochs }

    The hyperparameters used for the DPO fine-tuning job.

    • batch_size: optional "auto" or number

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • union_member_0: "auto"

      • union_member_1: number

    • beta: optional "auto" or number

      The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

      • union_member_0: "auto"

      • union_member_1: number

    • learning_rate_multiplier: optional "auto" or number

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • union_member_0: "auto"

      • union_member_1: number

    • n_epochs: optional "auto" or number

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • union_member_0: "auto"

      • union_member_1: number

Reinforcement Hyperparameters

• reinforcement_hyperparameters: object { batch_size, compute_multiplier, eval_interval, 4 more }

  The hyperparameters used for the reinforcement fine-tuning job.

  • batch_size: optional "auto" or number

    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

    • union_member_0: "auto"

    • union_member_1: number

  • compute_multiplier: optional "auto" or number

    Multiplier on amount of compute used for exploring search space during training.

    • union_member_0: "auto"

    • union_member_1: number

  • eval_interval: optional "auto" or number

    The number of training steps between evaluation runs.

    • union_member_0: "auto"

    • union_member_1: number

  • eval_samples: optional "auto" or number

    Number of evaluation samples to generate per training step.

    • union_member_0: "auto"

    • union_member_1: number

  • learning_rate_multiplier: optional "auto" or number

    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

    • union_member_0: "auto"

    • union_member_1: number

  • n_epochs: optional "auto" or number

    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

    • union_member_0: "auto"

    • union_member_1: number

  • reasoning_effort: optional "default" or "low" or "medium" or "high"

    Level of reasoning effort.

    • "default"

    • "low"

    • "medium"

    • "high"

Reinforcement Method

• reinforcement_method: object { grader, hyperparameters }

  Configuration for the reinforcement fine-tuning method.

  • grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

    The grader used for the fine-tuning job.

    • string_check_grader: object { input, name, operation, 2 more }

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      • input: string

        The input text. This may include template strings.

      • name: string

        The name of the grader.

      • operation: "eq" or "ne" or "like" or "ilike"

        The string check operation to perform. One of eq, ne, like, or ilike.

        • "eq"

        • "ne"

        • "like"

        • "ilike"

      • reference: string

        The reference text. This may include template strings.

      • type: "string_check"

        The object type, which is always string_check.

    • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more

        The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

        • "cosine"

        • "fuzzy_match"

        • "bleu"

        • "gleu"

        • "meteor"

        • "rouge_1"

        • "rouge_2"

        • "rouge_3"

        • "rouge_4"

        • "rouge_5"

        • "rouge_l"

      • input: string

        The text being graded.

      • name: string

        The name of the grader.

      • reference: string

        The text being graded against.

      • type: "text_similarity"

        The type of grader.

    • python_grader: object { name, source, type, image_tag }

      A PythonGrader object that runs a python script on the input.

      • name: string

        The name of the grader.

      • source: string

        The source code of the python script.

      • type: "python"

        The object type, which is always python.

      • image_tag: optional string

        The image tag to use for the python script.

    • score_model_grader: object { input, model, name, 3 more }

      A ScoreModelGrader object that uses a model to assign a score to the input.

      • input: array of object { content, role, type }

        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

        • content: string or ResponseInputText or object { text, type } or 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • Text input: string

            A text input to the model.

          • response_input_text: object { text, type }

            A text input to the model.

            • text: string

              The text input to the model.

            • type: "input_text"

              The type of the input item. Always input_text.

          • Output text: object { text, type }

            A text output from the model.

            • text: string

              The text output from the model.

            • type: "output_text"

              The type of the output text. Always output_text.

          • Input image: object { image_url, type, detail }

            An image input block used within EvalItem content arrays.

            • image_url: string

              The URL of the image input.

            • type: "input_image"

              The type of the image input. Always input_image.

            • detail: optional string

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • response_input_audio: object { input_audio, type }

            An audio input to the model.

            • input_audio: object { data, format }

              • data: string

                Base64-encoded audio data.

              • format: "mp3" or "wav"

                The format of the audio data. Currently supported formats are mp3 and wav.

                • "mp3"

                • "wav"

            • type: "input_audio"

              The type of the input item. Always input_audio.

          • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • Text input: string

              A text input to the model.

            • response_input_text: object { text, type }

              A text input to the model.

            • Output text: object { text, type }

              A text output from the model.

              • text: string

                The text output from the model.

              • type: "output_text"

                The type of the output text. Always output_text.

            • Input image: object { image_url, type, detail }

              An image input block used within EvalItem content arrays.

              • image_url: string

                The URL of the image input.

              • type: "input_image"

                The type of the image input. Always input_image.

              • detail: optional string

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • response_input_audio: object { input_audio, type }

              An audio input to the model.

        • role: "user" or "assistant" or "system" or "developer"

          The role of the message input. One of user, assistant, system, or developer.

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: optional "message"

          The type of the message input. Always message.

          • "message"

      • model: string

        The model to use for the evaluation.

      • name: string

        The name of the grader.

      • type: "score_model"

        The object type, which is always score_model.

      • range: optional array of number

        The range of the score. Defaults to [0, 1].

      • sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }

        The sampling parameters for the model.

        • max_completions_tokens: optional number

          The maximum number of tokens the grader model may generate in its response.

        • reasoning_effort: optional "none" or "minimal" or "low" or 3 more

          Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

          • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.

          • All models before gpt-5.1 default to medium reasoning effort, and do not support none.

          • The gpt-5-pro model defaults to (and only supports) high reasoning effort.

          • xhigh is supported for all models after gpt-5.1-codex-max.

          • "none"

          • "minimal"

          • "low"

          • "medium"

          • "high"

          • "xhigh"

        • seed: optional number

          A seed value to initialize the randomness, during sampling.

        • temperature: optional number

          A higher temperature increases randomness in the outputs.

        • top_p: optional number

          An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

    • multi_grader: object { calculate_output, graders, name, type }

      A MultiGrader object combines the output of multiple graders to produce a single score.

      • calculate_output: string

        A formula to calculate the output based on grader results.

      • graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

        • string_check_grader: object { input, name, operation, 2 more }

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

        • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

          A TextSimilarityGrader object which grades text based on similarity metrics.

        • python_grader: object { name, source, type, image_tag }

          A PythonGrader object that runs a python script on the input.

        • score_model_grader: object { input, model, name, 3 more }

          A ScoreModelGrader object that uses a model to assign a score to the input.

        • label_model_grader: object { input, labels, model, 3 more }

          A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

          • input: array of object { content, role, type }

            • content: string or ResponseInputText or object { text, type } or 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • Text input: string

                A text input to the model.

              • response_input_text: object { text, type }

                A text input to the model.

              • Output text: object { text, type }

                A text output from the model.

                • text: string

                  The text output from the model.

                • type: "output_text"

                  The type of the output text. Always output_text.

              • Input image: object { image_url, type, detail }

                An image input block used within EvalItem content arrays.

                • image_url: string

                  The URL of the image input.

                • type: "input_image"

                  The type of the image input. Always input_image.

                • detail: optional string

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • response_input_audio: object { input_audio, type }

                An audio input to the model.

              • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • role: "user" or "assistant" or "system" or "developer"

              The role of the message input. One of user, assistant, system, or developer.

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: optional "message"

              The type of the message input. Always message.

              • "message"

          • labels: array of string

            The labels to assign to each item in the evaluation.

          • model: string

            The model to use for the evaluation. Must support structured outputs.

          • name: string

            The name of the grader.

          • passing_labels: array of string

            The labels that indicate a passing result. Must be a subset of labels.

          • type: "label_model"

            The object type, which is always label_model.

      • name: string

        The name of the grader.

      • type: "multi"

        The object type, which is always multi.

  • hyperparameters: optional object { batch_size, compute_multiplier, eval_interval, 4 more }

    The hyperparameters used for the reinforcement fine-tuning job.

    • batch_size: optional "auto" or number

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • union_member_0: "auto"

      • union_member_1: number

    • compute_multiplier: optional "auto" or number

      Multiplier on amount of compute used for exploring search space during training.

      • union_member_0: "auto"

      • union_member_1: number

    • eval_interval: optional "auto" or number

      The number of training steps between evaluation runs.

      • union_member_0: "auto"

      • union_member_1: number

    • eval_samples: optional "auto" or number

      Number of evaluation samples to generate per training step.

      • union_member_0: "auto"

      • union_member_1: number

    • learning_rate_multiplier: optional "auto" or number

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • union_member_0: "auto"

      • union_member_1: number

    • n_epochs: optional "auto" or number

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • union_member_0: "auto"

      • union_member_1: number

    • reasoning_effort: optional "default" or "low" or "medium" or "high"

      Level of reasoning effort.

      • "default"

      • "low"

      • "medium"

      • "high"

Supervised Hyperparameters

• supervised_hyperparameters: object { batch_size, learning_rate_multiplier, n_epochs }

  The hyperparameters used for the fine-tuning job.

  • batch_size: optional "auto" or number

    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

    • union_member_0: "auto"

    • union_member_1: number

  • learning_rate_multiplier: optional "auto" or number

    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

    • union_member_0: "auto"

    • union_member_1: number

  • n_epochs: optional "auto" or number

    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

    • union_member_0: "auto"

    • union_member_1: number

Supervised Method

• supervised_method: object { hyperparameters }

  Configuration for the supervised fine-tuning method.

  • hyperparameters: optional object { batch_size, learning_rate_multiplier, n_epochs }

    The hyperparameters used for the fine-tuning job.

    • batch_size: optional "auto" or number

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • union_member_0: "auto"

      • union_member_1: number

    • learning_rate_multiplier: optional "auto" or number

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • union_member_0: "auto"

      • union_member_1: number

    • n_epochs: optional "auto" or number

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • union_member_0: "auto"

      • union_member_1: number

Jobs

Create fine-tuning job

$ openai fine-tuning:jobs create

post /fine_tuning/jobs

Creates a fine-tuning job which begins the process of creating a new model from a given dataset.

Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.

Learn more about fine-tuning

Parameters

• --model: string or "babbage-002" or "davinci-002" or "gpt-3.5-turbo" or "gpt-4o-mini"

  The name of the model to fine-tune. You can select one of the supported models.

• --training-file: string

  The ID of an uploaded file that contains training data.

  See upload file for how to upload a file.

  Your dataset must be formatted as a JSONL file. Additionally, you must upload your file with the purpose fine-tune.

  The contents of the file should differ depending on if the model uses the chat, completions format, or if the fine-tuning method uses the preference format.

  See the fine-tuning guide for more details.

• --hyperparameters: optional object { batch_size, learning_rate_multiplier, n_epochs }

  The hyperparameters used for the fine-tuning job. This value is now deprecated in favor of method, and should be passed in under the method parameter.

• --integration: optional array of object { type, wandb }

  A list of integrations to enable for your fine-tuning job.

• --metadata: optional map[string]

  Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

  Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

• --method: optional object { type, dpo, reinforcement, supervised }

  The method used for fine-tuning.

• --seed: optional number

  The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but may differ in rare cases. If a seed is not specified, one will be generated for you.

• --suffix: optional string

  A string of up to 64 characters that will be added to your fine-tuned model name.

  For example, a suffix of "custom-model-name" would produce a model name like ft:gpt-4o-mini:openai:custom-model-name:7p4lURel.

• --validation-file: optional string

  The ID of an uploaded file that contains validation data.

  If you provide this file, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in the fine-tuning results file. The same data should not be present in both train and validation files.

  Your dataset must be formatted as a JSONL file. You must upload your file with the purpose fine-tune.

  See the fine-tuning guide for more details.

Returns

• fine_tuning_job: object { id, created_at, error, 16 more }

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: string

    The object identifier, which can be referenced in the API endpoints.

  • created_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: object { code, message, param }

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: string

      A machine-readable error code.

    • message: string

      A human-readable error message.

    • param: string

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: string

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: object { batch_size, learning_rate_multiplier, n_epochs }

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: optional "auto" or number

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • union_member_0: "auto"

      • union_member_1: number

    • learning_rate_multiplier: optional "auto" or number

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • Auto: "auto"

      • union_member_1: number

    • n_epochs: optional "auto" or number

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • Auto: "auto"

      • union_member_1: number

  • model: string

    The base model that is being fine-tuned.

  • object: "fine_tuning.job"

    The object type, which is always "fine_tuning.job".

  • organization_id: string

    The organization that owns the fine-tuning job.

  • result_files: array of string

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: number

    The seed used for the fine-tuning job.

  • status: "validating_files" or "queued" or "running" or 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: number

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: string

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: string

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: optional number

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: optional array of FineTuningJobWandbIntegrationObject

    A list of integrations to enable for this fine-tuning job.

    • type: "wandb"

      The type of the integration being enabled for the fine-tuning job

    • wandb: object { project, entity, name, tags }

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: string

        The name of the project that the new run will be created under.

      • entity: optional string

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: optional string

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: optional array of string

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: optional map[string]

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • method: optional object { type, dpo, reinforcement, supervised }

    The method used for fine-tuning.

    • type: "supervised" or "dpo" or "reinforcement"

      The type of method. Is either supervised, dpo, or reinforcement.

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: optional object { hyperparameters }

      Configuration for the DPO fine-tuning method.

      • hyperparameters: optional object { batch_size, beta, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • beta: optional "auto" or number

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

    • reinforcement: optional object { grader, hyperparameters }

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

        The grader used for the fine-tuning job.

        • string_check_grader: object { input, name, operation, 2 more }

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: string

            The input text. This may include template strings.

          • name: string

            The name of the grader.

          • operation: "eq" or "ne" or "like" or "ilike"

            The string check operation to perform. One of eq, ne, like, or ilike.

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: string

            The reference text. This may include template strings.

          • type: "string_check"

            The object type, which is always string_check.

        • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: string

            The text being graded.

          • name: string

            The name of the grader.

          • reference: string

            The text being graded against.

          • type: "text_similarity"

            The type of grader.

        • python_grader: object { name, source, type, image_tag }

          A PythonGrader object that runs a python script on the input.

          • name: string

            The name of the grader.

          • source: string

            The source code of the python script.

          • type: "python"

            The object type, which is always python.

          • image_tag: optional string

            The image tag to use for the python script.

        • score_model_grader: object { input, model, name, 3 more }

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: array of object { content, role, type }

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: string or ResponseInputText or object { text, type } or 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • Text input: string

                A text input to the model.

              • response_input_text: object { text, type }

                A text input to the model.

                • text: string

                  The text input to the model.

                • type: "input_text"

                  The type of the input item. Always input_text.

              • Output text: object { text, type }

                A text output from the model.

                • text: string

                  The text output from the model.

                • type: "output_text"

                  The type of the output text. Always output_text.

              • Input image: object { image_url, type, detail }

                An image input block used within EvalItem content arrays.

                • image_url: string

                  The URL of the image input.

                • type: "input_image"

                  The type of the image input. Always input_image.

                • detail: optional string

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • response_input_audio: object { input_audio, type }

                An audio input to the model.

                • input_audio: object { data, format }

                  • data: string

                    Base64-encoded audio data.

                  • format: "mp3" or "wav"

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • "mp3"

                    • "wav"

                • type: "input_audio"

                  The type of the input item. Always input_audio.

              • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • Text input: string

                  A text input to the model.

                • response_input_text: object { text, type }

                  A text input to the model.

                • Output text: object { text, type }

                  A text output from the model.

                  • text: string

                    The text output from the model.

                  • type: "output_text"

                    The type of the output text. Always output_text.

                • Input image: object { image_url, type, detail }

                  An image input block used within EvalItem content arrays.

                  • image_url: string

                    The URL of the image input.

                  • type: "input_image"

                    The type of the image input. Always input_image.

                  • detail: optional string

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • response_input_audio: object { input_audio, type }

                  An audio input to the model.

            • role: "user" or "assistant" or "system" or "developer"

              The role of the message input. One of user, assistant, system, or developer.

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: optional "message"

              The type of the message input. Always message.

              • "message"

          • model: string

            The model to use for the evaluation.

          • name: string

            The name of the grader.

          • type: "score_model"

            The object type, which is always score_model.

          • range: optional array of number

            The range of the score. Defaults to [0, 1].

          • sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }

            The sampling parameters for the model.

            • max_completions_tokens: optional number

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: optional "none" or "minimal" or "low" or 3 more

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

              • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.

              • All models before gpt-5.1 default to medium reasoning effort, and do not support none.

              • The gpt-5-pro model defaults to (and only supports) high reasoning effort.

              • xhigh is supported for all models after gpt-5.1-codex-max.

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

            • seed: optional number

              A seed value to initialize the randomness, during sampling.

            • temperature: optional number

              A higher temperature increases randomness in the outputs.

            • top_p: optional number

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • multi_grader: object { calculate_output, graders, name, type }

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: string

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • string_check_grader: object { input, name, operation, 2 more }

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • python_grader: object { name, source, type, image_tag }

              A PythonGrader object that runs a python script on the input.

            • score_model_grader: object { input, model, name, 3 more }

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • label_model_grader: object { input, labels, model, 3 more }

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: array of object { content, role, type }

                • content: string or ResponseInputText or object { text, type } or 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • Text input: string

                    A text input to the model.

                  • response_input_text: object { text, type }

                    A text input to the model.

                  • Output text: object { text, type }

                    A text output from the model.

                    • text: string

                      The text output from the model.

                    • type: "output_text"

                      The type of the output text. Always output_text.

                  • Input image: object { image_url, type, detail }

                    An image input block used within EvalItem content arrays.

                    • image_url: string

                      The URL of the image input.

                    • type: "input_image"

                      The type of the image input. Always input_image.

                    • detail: optional string

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • response_input_audio: object { input_audio, type }

                    An audio input to the model.

                  • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: "user" or "assistant" or "system" or "developer"

                  The role of the message input. One of user, assistant, system, or developer.

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: optional "message"

                  The type of the message input. Always message.

                  • "message"

              • labels: array of string

                The labels to assign to each item in the evaluation.

              • model: string

                The model to use for the evaluation. Must support structured outputs.

              • name: string

                The name of the grader.

              • passing_labels: array of string

                The labels that indicate a passing result. Must be a subset of labels.

              • type: "label_model"

                The object type, which is always label_model.

          • name: string

            The name of the grader.

          • type: "multi"

            The object type, which is always multi.

      • hyperparameters: optional object { batch_size, compute_multiplier, eval_interval, 4 more }

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • compute_multiplier: optional "auto" or number

          Multiplier on amount of compute used for exploring search space during training.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_interval: optional "auto" or number

          The number of training steps between evaluation runs.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_samples: optional "auto" or number

          Number of evaluation samples to generate per training step.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

        • reasoning_effort: optional "default" or "low" or "medium" or "high"

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: optional object { hyperparameters }

      Configuration for the supervised fine-tuning method.

      • hyperparameters: optional object { batch_size, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

Example

openai fine-tuning:jobs create \
  --api-key 'My API Key' \
  --model gpt-4o-mini \
  --training-file file-abc123

Response

{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}

List fine-tuning jobs

$ openai fine-tuning:jobs list

get /fine_tuning/jobs

List your organization's fine-tuning jobs

Parameters

• --after: optional string

  Identifier for the last job from the previous pagination request.

• --limit: optional number

  Number of fine-tuning jobs to retrieve.

• --metadata: optional map[string]

  Optional metadata filter. To filter, use the syntax metadata[k]=v. Alternatively, set metadata=null to indicate no metadata.

Returns

• ListPaginatedFineTuningJobsResponse: object { data, has_more, object }

  • data: array of FineTuningJob

    • id: string

      The object identifier, which can be referenced in the API endpoints.

    • created_at: number

      The Unix timestamp (in seconds) for when the fine-tuning job was created.

    • error: object { code, message, param }

      For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

      • code: string

        A machine-readable error code.

      • message: string

        A human-readable error message.

      • param: string

        The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

    • fine_tuned_model: string

      The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

    • finished_at: number

      The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

    • hyperparameters: object { batch_size, learning_rate_multiplier, n_epochs }

      The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

      • batch_size: optional "auto" or number

        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

        • union_member_0: "auto"

        • union_member_1: number

      • learning_rate_multiplier: optional "auto" or number

        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

        • Auto: "auto"

        • union_member_1: number

      • n_epochs: optional "auto" or number

        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

        • Auto: "auto"

        • union_member_1: number

    • model: string

      The base model that is being fine-tuned.

    • object: "fine_tuning.job"

      The object type, which is always "fine_tuning.job".

    • organization_id: string

      The organization that owns the fine-tuning job.

    • result_files: array of string

      The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

    • seed: number

      The seed used for the fine-tuning job.

    • status: "validating_files" or "queued" or "running" or 3 more

      The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

      • "validating_files"

      • "queued"

      • "running"

      • "succeeded"

      • "failed"

      • "cancelled"

    • trained_tokens: number

      The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

    • training_file: string

      The file ID used for training. You can retrieve the training data with the Files API.

    • validation_file: string

      The file ID used for validation. You can retrieve the validation results with the Files API.

    • estimated_finish: optional number

      The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

    • integrations: optional array of FineTuningJobWandbIntegrationObject

      A list of integrations to enable for this fine-tuning job.

      • type: "wandb"

        The type of the integration being enabled for the fine-tuning job

      • wandb: object { project, entity, name, tags }

        The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

        • project: string

          The name of the project that the new run will be created under.

        • entity: optional string

          The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

        • name: optional string

          A display name to set for the run. If not set, we will use the Job ID as the name.

        • tags: optional array of string

          A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

    • metadata: optional map[string]

      Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

      Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

    • method: optional object { type, dpo, reinforcement, supervised }

      The method used for fine-tuning.

      • type: "supervised" or "dpo" or "reinforcement"

        The type of method. Is either supervised, dpo, or reinforcement.

        • "supervised"

        • "dpo"

        • "reinforcement"

      • dpo: optional object { hyperparameters }

        Configuration for the DPO fine-tuning method.

        • hyperparameters: optional object { batch_size, beta, learning_rate_multiplier, n_epochs }

          The hyperparameters used for the DPO fine-tuning job.

          • batch_size: optional "auto" or number

            Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

            • union_member_0: "auto"

            • union_member_1: number

          • beta: optional "auto" or number

            The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

            • union_member_0: "auto"

            • union_member_1: number

          • learning_rate_multiplier: optional "auto" or number

            Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

            • union_member_0: "auto"

            • union_member_1: number

          • n_epochs: optional "auto" or number

            The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

            • union_member_0: "auto"

            • union_member_1: number

      • reinforcement: optional object { grader, hyperparameters }

        Configuration for the reinforcement fine-tuning method.

        • grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

          The grader used for the fine-tuning job.

          • string_check_grader: object { input, name, operation, 2 more }

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • input: string

              The input text. This may include template strings.

            • name: string

              The name of the grader.

            • operation: "eq" or "ne" or "like" or "ilike"

              The string check operation to perform. One of eq, ne, like, or ilike.

              • "eq"

              • "ne"

              • "like"

              • "ilike"

            • reference: string

              The reference text. This may include template strings.

            • type: "string_check"

              The object type, which is always string_check.

          • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

            A TextSimilarityGrader object which grades text based on similarity metrics.

            • evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more

              The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

              • "cosine"

              • "fuzzy_match"

              • "bleu"

              • "gleu"

              • "meteor"

              • "rouge_1"

              • "rouge_2"

              • "rouge_3"

              • "rouge_4"

              • "rouge_5"

              • "rouge_l"

            • input: string

              The text being graded.

            • name: string

              The name of the grader.

            • reference: string

              The text being graded against.

            • type: "text_similarity"

              The type of grader.

          • python_grader: object { name, source, type, image_tag }

            A PythonGrader object that runs a python script on the input.

            • name: string

              The name of the grader.

            • source: string

              The source code of the python script.

            • type: "python"

              The object type, which is always python.

            • image_tag: optional string

              The image tag to use for the python script.

          • score_model_grader: object { input, model, name, 3 more }

            A ScoreModelGrader object that uses a model to assign a score to the input.

            • input: array of object { content, role, type }

              The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

              • content: string or ResponseInputText or object { text, type } or 3 more

                Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                • Text input: string

                  A text input to the model.

                • response_input_text: object { text, type }

                  A text input to the model.

                  • text: string

                    The text input to the model.

                  • type: "input_text"

                    The type of the input item. Always input_text.

                • Output text: object { text, type }

                  A text output from the model.

                  • text: string

                    The text output from the model.

                  • type: "output_text"

                    The type of the output text. Always output_text.

                • Input image: object { image_url, type, detail }

                  An image input block used within EvalItem content arrays.

                  • image_url: string

                    The URL of the image input.

                  • type: "input_image"

                    The type of the image input. Always input_image.

                  • detail: optional string

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • response_input_audio: object { input_audio, type }

                  An audio input to the model.

                  • input_audio: object { data, format }

                    • data: string

                      Base64-encoded audio data.

                    • format: "mp3" or "wav"

                      The format of the audio data. Currently supported formats are mp3 and wav.

                      • "mp3"

                      • "wav"

                  • type: "input_audio"

                    The type of the input item. Always input_audio.

                • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                  A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                  • Text input: string

                    A text input to the model.

                  • response_input_text: object { text, type }

                    A text input to the model.

                  • Output text: object { text, type }

                    A text output from the model.

                    • text: string

                      The text output from the model.

                    • type: "output_text"

                      The type of the output text. Always output_text.

                  • Input image: object { image_url, type, detail }

                    An image input block used within EvalItem content arrays.

                    • image_url: string

                      The URL of the image input.

                    • type: "input_image"

                      The type of the image input. Always input_image.

                    • detail: optional string

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • response_input_audio: object { input_audio, type }

                    An audio input to the model.

              • role: "user" or "assistant" or "system" or "developer"

                The role of the message input. One of user, assistant, system, or developer.

                • "user"

                • "assistant"

                • "system"

                • "developer"

              • type: optional "message"

                The type of the message input. Always message.

                • "message"

            • model: string

              The model to use for the evaluation.

            • name: string

              The name of the grader.

            • type: "score_model"

              The object type, which is always score_model.

            • range: optional array of number

              The range of the score. Defaults to [0, 1].

            • sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }

              The sampling parameters for the model.

              • max_completions_tokens: optional number

                The maximum number of tokens the grader model may generate in its response.

              • reasoning_effort: optional "none" or "minimal" or "low" or 3 more

                Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

                • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.

                • All models before gpt-5.1 default to medium reasoning effort, and do not support none.

                • The gpt-5-pro model defaults to (and only supports) high reasoning effort.

                • xhigh is supported for all models after gpt-5.1-codex-max.

                • "none"

                • "minimal"

                • "low"

                • "medium"

                • "high"

                • "xhigh"

              • seed: optional number

                A seed value to initialize the randomness, during sampling.

              • temperature: optional number

                A higher temperature increases randomness in the outputs.

              • top_p: optional number

                An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

          • multi_grader: object { calculate_output, graders, name, type }

            A MultiGrader object combines the output of multiple graders to produce a single score.

            • calculate_output: string

              A formula to calculate the output based on grader results.

            • graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

              • string_check_grader: object { input, name, operation, 2 more }

                A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

              • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

                A TextSimilarityGrader object which grades text based on similarity metrics.

              • python_grader: object { name, source, type, image_tag }

                A PythonGrader object that runs a python script on the input.

              • score_model_grader: object { input, model, name, 3 more }

                A ScoreModelGrader object that uses a model to assign a score to the input.

              • label_model_grader: object { input, labels, model, 3 more }

                A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

                • input: array of object { content, role, type }

                  • content: string or ResponseInputText or object { text, type } or 3 more

                    Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                    • Text input: string

                      A text input to the model.

                    • response_input_text: object { text, type }

                      A text input to the model.

                    • Output text: object { text, type }

                      A text output from the model.

                      • text: string

                        The text output from the model.

                      • type: "output_text"

                        The type of the output text. Always output_text.

                    • Input image: object { image_url, type, detail }

                      An image input block used within EvalItem content arrays.

                      • image_url: string

                        The URL of the image input.

                      • type: "input_image"

                        The type of the image input. Always input_image.

                      • detail: optional string

                        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                    • response_input_audio: object { input_audio, type }

                      An audio input to the model.

                    • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                      A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                  • role: "user" or "assistant" or "system" or "developer"

                    The role of the message input. One of user, assistant, system, or developer.

                    • "user"

                    • "assistant"

                    • "system"

                    • "developer"

                  • type: optional "message"

                    The type of the message input. Always message.

                    • "message"

                • labels: array of string

                  The labels to assign to each item in the evaluation.

                • model: string

                  The model to use for the evaluation. Must support structured outputs.

                • name: string

                  The name of the grader.

                • passing_labels: array of string

                  The labels that indicate a passing result. Must be a subset of labels.

                • type: "label_model"

                  The object type, which is always label_model.

            • name: string

              The name of the grader.

            • type: "multi"

              The object type, which is always multi.

        • hyperparameters: optional object { batch_size, compute_multiplier, eval_interval, 4 more }

          The hyperparameters used for the reinforcement fine-tuning job.

          • batch_size: optional "auto" or number

            Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

            • union_member_0: "auto"

            • union_member_1: number

          • compute_multiplier: optional "auto" or number

            Multiplier on amount of compute used for exploring search space during training.

            • union_member_0: "auto"

            • union_member_1: number

          • eval_interval: optional "auto" or number

            The number of training steps between evaluation runs.

            • union_member_0: "auto"

            • union_member_1: number

          • eval_samples: optional "auto" or number

            Number of evaluation samples to generate per training step.

            • union_member_0: "auto"

            • union_member_1: number

          • learning_rate_multiplier: optional "auto" or number

            Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

            • union_member_0: "auto"

            • union_member_1: number

          • n_epochs: optional "auto" or number

            The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

            • union_member_0: "auto"

            • union_member_1: number

          • reasoning_effort: optional "default" or "low" or "medium" or "high"

            Level of reasoning effort.

            • "default"

            • "low"

            • "medium"

            • "high"

      • supervised: optional object { hyperparameters }

        Configuration for the supervised fine-tuning method.

        • hyperparameters: optional object { batch_size, learning_rate_multiplier, n_epochs }

          The hyperparameters used for the fine-tuning job.

          • batch_size: optional "auto" or number

            Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

            • union_member_0: "auto"

            • union_member_1: number

          • learning_rate_multiplier: optional "auto" or number

            Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

            • union_member_0: "auto"

            • union_member_1: number

          • n_epochs: optional "auto" or number

            The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

            • union_member_0: "auto"

            • union_member_1: number

  • has_more: boolean

  • object: "list"

Example

openai fine-tuning:jobs list \
  --api-key 'My API Key'

Response

{
  "data": [
    {
      "id": "id",
      "created_at": 0,
      "error": {
        "code": "code",
        "message": "message",
        "param": "param"
      },
      "fine_tuned_model": "fine_tuned_model",
      "finished_at": 0,
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      },
      "model": "model",
      "object": "fine_tuning.job",
      "organization_id": "organization_id",
      "result_files": [
        "file-abc123"
      ],
      "seed": 0,
      "status": "validating_files",
      "trained_tokens": 0,
      "training_file": "training_file",
      "validation_file": "validation_file",
      "estimated_finish": 0,
      "integrations": [
        {
          "type": "wandb",
          "wandb": {
            "project": "my-wandb-project",
            "entity": "entity",
            "name": "name",
            "tags": [
              "custom-tag"
            ]
          }
        }
      ],
      "metadata": {
        "foo": "string"
      },
      "method": {
        "type": "supervised",
        "dpo": {
          "hyperparameters": {
            "batch_size": "auto",
            "beta": "auto",
            "learning_rate_multiplier": "auto",
            "n_epochs": "auto"
          }
        },
        "reinforcement": {
          "grader": {
            "input": "input",
            "name": "name",
            "operation": "eq",
            "reference": "reference",
            "type": "string_check"
          },
          "hyperparameters": {
            "batch_size": "auto",
            "compute_multiplier": "auto",
            "eval_interval": "auto",
            "eval_samples": "auto",
            "learning_rate_multiplier": "auto",
            "n_epochs": "auto",
            "reasoning_effort": "default"
          }
        },
        "supervised": {
          "hyperparameters": {
            "batch_size": "auto",
            "learning_rate_multiplier": "auto",
            "n_epochs": "auto"
          }
        }
      }
    }
  ],
  "has_more": true,
  "object": "list"
}

Retrieve fine-tuning job

$ openai fine-tuning:jobs retrieve

get /fine_tuning/jobs/{fine_tuning_job_id}

Get info about a fine-tuning job.

Learn more about fine-tuning

Parameters

• --fine-tuning-job-id: string

  The ID of the fine-tuning job.

Returns

• fine_tuning_job: object { id, created_at, error, 16 more }

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: string

    The object identifier, which can be referenced in the API endpoints.

  • created_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: object { code, message, param }

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: string

      A machine-readable error code.

    • message: string

      A human-readable error message.

    • param: string

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: string

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: object { batch_size, learning_rate_multiplier, n_epochs }

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: optional "auto" or number

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • union_member_0: "auto"

      • union_member_1: number

    • learning_rate_multiplier: optional "auto" or number

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • Auto: "auto"

      • union_member_1: number

    • n_epochs: optional "auto" or number

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • Auto: "auto"

      • union_member_1: number

  • model: string

    The base model that is being fine-tuned.

  • object: "fine_tuning.job"

    The object type, which is always "fine_tuning.job".

  • organization_id: string

    The organization that owns the fine-tuning job.

  • result_files: array of string

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: number

    The seed used for the fine-tuning job.

  • status: "validating_files" or "queued" or "running" or 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: number

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: string

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: string

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: optional number

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: optional array of FineTuningJobWandbIntegrationObject

    A list of integrations to enable for this fine-tuning job.

    • type: "wandb"

      The type of the integration being enabled for the fine-tuning job

    • wandb: object { project, entity, name, tags }

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: string

        The name of the project that the new run will be created under.

      • entity: optional string

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: optional string

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: optional array of string

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: optional map[string]

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • method: optional object { type, dpo, reinforcement, supervised }

    The method used for fine-tuning.

    • type: "supervised" or "dpo" or "reinforcement"

      The type of method. Is either supervised, dpo, or reinforcement.

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: optional object { hyperparameters }

      Configuration for the DPO fine-tuning method.

      • hyperparameters: optional object { batch_size, beta, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • beta: optional "auto" or number

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

    • reinforcement: optional object { grader, hyperparameters }

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

        The grader used for the fine-tuning job.

        • string_check_grader: object { input, name, operation, 2 more }

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: string

            The input text. This may include template strings.

          • name: string

            The name of the grader.

          • operation: "eq" or "ne" or "like" or "ilike"

            The string check operation to perform. One of eq, ne, like, or ilike.

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: string

            The reference text. This may include template strings.

          • type: "string_check"

            The object type, which is always string_check.

        • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: string

            The text being graded.

          • name: string

            The name of the grader.

          • reference: string

            The text being graded against.

          • type: "text_similarity"

            The type of grader.

        • python_grader: object { name, source, type, image_tag }

          A PythonGrader object that runs a python script on the input.

          • name: string

            The name of the grader.

          • source: string

            The source code of the python script.

          • type: "python"

            The object type, which is always python.

          • image_tag: optional string

            The image tag to use for the python script.

        • score_model_grader: object { input, model, name, 3 more }

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: array of object { content, role, type }

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: string or ResponseInputText or object { text, type } or 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • Text input: string

                A text input to the model.

              • response_input_text: object { text, type }

                A text input to the model.

                • text: string

                  The text input to the model.

                • type: "input_text"

                  The type of the input item. Always input_text.

              • Output text: object { text, type }

                A text output from the model.

                • text: string

                  The text output from the model.

                • type: "output_text"

                  The type of the output text. Always output_text.

              • Input image: object { image_url, type, detail }

                An image input block used within EvalItem content arrays.

                • image_url: string

                  The URL of the image input.

                • type: "input_image"

                  The type of the image input. Always input_image.

                • detail: optional string

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • response_input_audio: object { input_audio, type }

                An audio input to the model.

                • input_audio: object { data, format }

                  • data: string

                    Base64-encoded audio data.

                  • format: "mp3" or "wav"

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • "mp3"

                    • "wav"

                • type: "input_audio"

                  The type of the input item. Always input_audio.

              • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • Text input: string

                  A text input to the model.

                • response_input_text: object { text, type }

                  A text input to the model.

                • Output text: object { text, type }

                  A text output from the model.

                  • text: string

                    The text output from the model.

                  • type: "output_text"

                    The type of the output text. Always output_text.

                • Input image: object { image_url, type, detail }

                  An image input block used within EvalItem content arrays.

                  • image_url: string

                    The URL of the image input.

                  • type: "input_image"

                    The type of the image input. Always input_image.

                  • detail: optional string

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • response_input_audio: object { input_audio, type }

                  An audio input to the model.

            • role: "user" or "assistant" or "system" or "developer"

              The role of the message input. One of user, assistant, system, or developer.

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: optional "message"

              The type of the message input. Always message.

              • "message"

          • model: string

            The model to use for the evaluation.

          • name: string

            The name of the grader.

          • type: "score_model"

            The object type, which is always score_model.

          • range: optional array of number

            The range of the score. Defaults to [0, 1].

          • sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }

            The sampling parameters for the model.

            • max_completions_tokens: optional number

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: optional "none" or "minimal" or "low" or 3 more

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

              • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.

              • All models before gpt-5.1 default to medium reasoning effort, and do not support none.

              • The gpt-5-pro model defaults to (and only supports) high reasoning effort.

              • xhigh is supported for all models after gpt-5.1-codex-max.

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

            • seed: optional number

              A seed value to initialize the randomness, during sampling.

            • temperature: optional number

              A higher temperature increases randomness in the outputs.

            • top_p: optional number

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • multi_grader: object { calculate_output, graders, name, type }

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: string

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • string_check_grader: object { input, name, operation, 2 more }

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • python_grader: object { name, source, type, image_tag }

              A PythonGrader object that runs a python script on the input.

            • score_model_grader: object { input, model, name, 3 more }

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • label_model_grader: object { input, labels, model, 3 more }

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: array of object { content, role, type }

                • content: string or ResponseInputText or object { text, type } or 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • Text input: string

                    A text input to the model.

                  • response_input_text: object { text, type }

                    A text input to the model.

                  • Output text: object { text, type }

                    A text output from the model.

                    • text: string

                      The text output from the model.

                    • type: "output_text"

                      The type of the output text. Always output_text.

                  • Input image: object { image_url, type, detail }

                    An image input block used within EvalItem content arrays.

                    • image_url: string

                      The URL of the image input.

                    • type: "input_image"

                      The type of the image input. Always input_image.

                    • detail: optional string

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • response_input_audio: object { input_audio, type }

                    An audio input to the model.

                  • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: "user" or "assistant" or "system" or "developer"

                  The role of the message input. One of user, assistant, system, or developer.

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: optional "message"

                  The type of the message input. Always message.

                  • "message"

              • labels: array of string

                The labels to assign to each item in the evaluation.

              • model: string

                The model to use for the evaluation. Must support structured outputs.

              • name: string

                The name of the grader.

              • passing_labels: array of string

                The labels that indicate a passing result. Must be a subset of labels.

              • type: "label_model"

                The object type, which is always label_model.

          • name: string

            The name of the grader.

          • type: "multi"

            The object type, which is always multi.

      • hyperparameters: optional object { batch_size, compute_multiplier, eval_interval, 4 more }

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • compute_multiplier: optional "auto" or number

          Multiplier on amount of compute used for exploring search space during training.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_interval: optional "auto" or number

          The number of training steps between evaluation runs.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_samples: optional "auto" or number

          Number of evaluation samples to generate per training step.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

        • reasoning_effort: optional "default" or "low" or "medium" or "high"

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: optional object { hyperparameters }

      Configuration for the supervised fine-tuning method.

      • hyperparameters: optional object { batch_size, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

Example

openai fine-tuning:jobs retrieve \
  --api-key 'My API Key' \
  --fine-tuning-job-id ft-AF1WoRqd3aJAHsqc9NY7iL8F

Response

{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}

List fine-tuning events

$ openai fine-tuning:jobs list-events

get /fine_tuning/jobs/{fine_tuning_job_id}/events

Get status updates for a fine-tuning job.

Parameters

• --fine-tuning-job-id: string

  The ID of the fine-tuning job to get events for.

• --after: optional string

  Identifier for the last event from the previous pagination request.

• --limit: optional number

  Number of events to retrieve.

Returns

• ListFineTuningJobEventsResponse: object { data, has_more, object }

  • data: array of FineTuningJobEvent

    • id: string

      The object identifier.

    • created_at: number

      The Unix timestamp (in seconds) for when the fine-tuning job was created.

    • level: "info" or "warn" or "error"

      The log level of the event.

      • "info"

      • "warn"

      • "error"

    • message: string

      The message of the event.

    • object: "fine_tuning.job.event"

      The object type, which is always "fine_tuning.job.event".

    • data: optional unknown

      The data associated with the event.

    • type: optional "message" or "metrics"

      The type of event.

      • "message"

      • "metrics"

  • has_more: boolean

  • object: "list"

Example

openai fine-tuning:jobs list-events \
  --api-key 'My API Key' \
  --fine-tuning-job-id ft-AF1WoRqd3aJAHsqc9NY7iL8F

Response

{
  "data": [
    {
      "id": "id",
      "created_at": 0,
      "level": "info",
      "message": "message",
      "object": "fine_tuning.job.event",
      "data": {},
      "type": "message"
    }
  ],
  "has_more": true,
  "object": "list"
}

Cancel fine-tuning

$ openai fine-tuning:jobs cancel

post /fine_tuning/jobs/{fine_tuning_job_id}/cancel

Immediately cancel a fine-tune job.

Parameters

• --fine-tuning-job-id: string

  The ID of the fine-tuning job to cancel.

Returns

• fine_tuning_job: object { id, created_at, error, 16 more }

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: string

    The object identifier, which can be referenced in the API endpoints.

  • created_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: object { code, message, param }

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: string

      A machine-readable error code.

    • message: string

      A human-readable error message.

    • param: string

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: string

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: object { batch_size, learning_rate_multiplier, n_epochs }

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: optional "auto" or number

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • union_member_0: "auto"

      • union_member_1: number

    • learning_rate_multiplier: optional "auto" or number

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • Auto: "auto"

      • union_member_1: number

    • n_epochs: optional "auto" or number

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • Auto: "auto"

      • union_member_1: number

  • model: string

    The base model that is being fine-tuned.

  • object: "fine_tuning.job"

    The object type, which is always "fine_tuning.job".

  • organization_id: string

    The organization that owns the fine-tuning job.

  • result_files: array of string

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: number

    The seed used for the fine-tuning job.

  • status: "validating_files" or "queued" or "running" or 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: number

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: string

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: string

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: optional number

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: optional array of FineTuningJobWandbIntegrationObject

    A list of integrations to enable for this fine-tuning job.

    • type: "wandb"

      The type of the integration being enabled for the fine-tuning job

    • wandb: object { project, entity, name, tags }

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: string

        The name of the project that the new run will be created under.

      • entity: optional string

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: optional string

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: optional array of string

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: optional map[string]

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • method: optional object { type, dpo, reinforcement, supervised }

    The method used for fine-tuning.

    • type: "supervised" or "dpo" or "reinforcement"

      The type of method. Is either supervised, dpo, or reinforcement.

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: optional object { hyperparameters }

      Configuration for the DPO fine-tuning method.

      • hyperparameters: optional object { batch_size, beta, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • beta: optional "auto" or number

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

    • reinforcement: optional object { grader, hyperparameters }

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

        The grader used for the fine-tuning job.

        • string_check_grader: object { input, name, operation, 2 more }

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: string

            The input text. This may include template strings.

          • name: string

            The name of the grader.

          • operation: "eq" or "ne" or "like" or "ilike"

            The string check operation to perform. One of eq, ne, like, or ilike.

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: string

            The reference text. This may include template strings.

          • type: "string_check"

            The object type, which is always string_check.

        • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: string

            The text being graded.

          • name: string

            The name of the grader.

          • reference: string

            The text being graded against.

          • type: "text_similarity"

            The type of grader.

        • python_grader: object { name, source, type, image_tag }

          A PythonGrader object that runs a python script on the input.

          • name: string

            The name of the grader.

          • source: string

            The source code of the python script.

          • type: "python"

            The object type, which is always python.

          • image_tag: optional string

            The image tag to use for the python script.

        • score_model_grader: object { input, model, name, 3 more }

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: array of object { content, role, type }

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: string or ResponseInputText or object { text, type } or 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • Text input: string

                A text input to the model.

              • response_input_text: object { text, type }

                A text input to the model.

                • text: string

                  The text input to the model.

                • type: "input_text"

                  The type of the input item. Always input_text.

              • Output text: object { text, type }

                A text output from the model.

                • text: string

                  The text output from the model.

                • type: "output_text"

                  The type of the output text. Always output_text.

              • Input image: object { image_url, type, detail }

                An image input block used within EvalItem content arrays.

                • image_url: string

                  The URL of the image input.

                • type: "input_image"

                  The type of the image input. Always input_image.

                • detail: optional string

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • response_input_audio: object { input_audio, type }

                An audio input to the model.

                • input_audio: object { data, format }

                  • data: string

                    Base64-encoded audio data.

                  • format: "mp3" or "wav"

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • "mp3"

                    • "wav"

                • type: "input_audio"

                  The type of the input item. Always input_audio.

              • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • Text input: string

                  A text input to the model.

                • response_input_text: object { text, type }

                  A text input to the model.

                • Output text: object { text, type }

                  A text output from the model.

                  • text: string

                    The text output from the model.

                  • type: "output_text"

                    The type of the output text. Always output_text.

                • Input image: object { image_url, type, detail }

                  An image input block used within EvalItem content arrays.

                  • image_url: string

                    The URL of the image input.

                  • type: "input_image"

                    The type of the image input. Always input_image.

                  • detail: optional string

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • response_input_audio: object { input_audio, type }

                  An audio input to the model.

            • role: "user" or "assistant" or "system" or "developer"

              The role of the message input. One of user, assistant, system, or developer.

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: optional "message"

              The type of the message input. Always message.

              • "message"

          • model: string

            The model to use for the evaluation.

          • name: string

            The name of the grader.

          • type: "score_model"

            The object type, which is always score_model.

          • range: optional array of number

            The range of the score. Defaults to [0, 1].

          • sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }

            The sampling parameters for the model.

            • max_completions_tokens: optional number

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: optional "none" or "minimal" or "low" or 3 more

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

              • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.

              • All models before gpt-5.1 default to medium reasoning effort, and do not support none.

              • The gpt-5-pro model defaults to (and only supports) high reasoning effort.

              • xhigh is supported for all models after gpt-5.1-codex-max.

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

            • seed: optional number

              A seed value to initialize the randomness, during sampling.

            • temperature: optional number

              A higher temperature increases randomness in the outputs.

            • top_p: optional number

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • multi_grader: object { calculate_output, graders, name, type }

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: string

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • string_check_grader: object { input, name, operation, 2 more }

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • python_grader: object { name, source, type, image_tag }

              A PythonGrader object that runs a python script on the input.

            • score_model_grader: object { input, model, name, 3 more }

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • label_model_grader: object { input, labels, model, 3 more }

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: array of object { content, role, type }

                • content: string or ResponseInputText or object { text, type } or 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • Text input: string

                    A text input to the model.

                  • response_input_text: object { text, type }

                    A text input to the model.

                  • Output text: object { text, type }

                    A text output from the model.

                    • text: string

                      The text output from the model.

                    • type: "output_text"

                      The type of the output text. Always output_text.

                  • Input image: object { image_url, type, detail }

                    An image input block used within EvalItem content arrays.

                    • image_url: string

                      The URL of the image input.

                    • type: "input_image"

                      The type of the image input. Always input_image.

                    • detail: optional string

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • response_input_audio: object { input_audio, type }

                    An audio input to the model.

                  • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: "user" or "assistant" or "system" or "developer"

                  The role of the message input. One of user, assistant, system, or developer.

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: optional "message"

                  The type of the message input. Always message.

                  • "message"

              • labels: array of string

                The labels to assign to each item in the evaluation.

              • model: string

                The model to use for the evaluation. Must support structured outputs.

              • name: string

                The name of the grader.

              • passing_labels: array of string

                The labels that indicate a passing result. Must be a subset of labels.

              • type: "label_model"

                The object type, which is always label_model.

          • name: string

            The name of the grader.

          • type: "multi"

            The object type, which is always multi.

      • hyperparameters: optional object { batch_size, compute_multiplier, eval_interval, 4 more }

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • compute_multiplier: optional "auto" or number

          Multiplier on amount of compute used for exploring search space during training.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_interval: optional "auto" or number

          The number of training steps between evaluation runs.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_samples: optional "auto" or number

          Number of evaluation samples to generate per training step.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

        • reasoning_effort: optional "default" or "low" or "medium" or "high"

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: optional object { hyperparameters }

      Configuration for the supervised fine-tuning method.

      • hyperparameters: optional object { batch_size, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

Example

openai fine-tuning:jobs cancel \
  --api-key 'My API Key' \
  --fine-tuning-job-id ft-AF1WoRqd3aJAHsqc9NY7iL8F

Response

{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}

Pause fine-tuning

$ openai fine-tuning:jobs pause

post /fine_tuning/jobs/{fine_tuning_job_id}/pause

Pause a fine-tune job.

Parameters

• --fine-tuning-job-id: string

  The ID of the fine-tuning job to pause.

Returns

• fine_tuning_job: object { id, created_at, error, 16 more }

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: string

    The object identifier, which can be referenced in the API endpoints.

  • created_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: object { code, message, param }

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: string

      A machine-readable error code.

    • message: string

      A human-readable error message.

    • param: string

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: string

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: object { batch_size, learning_rate_multiplier, n_epochs }

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: optional "auto" or number

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • union_member_0: "auto"

      • union_member_1: number

    • learning_rate_multiplier: optional "auto" or number

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • Auto: "auto"

      • union_member_1: number

    • n_epochs: optional "auto" or number

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • Auto: "auto"

      • union_member_1: number

  • model: string

    The base model that is being fine-tuned.

  • object: "fine_tuning.job"

    The object type, which is always "fine_tuning.job".

  • organization_id: string

    The organization that owns the fine-tuning job.

  • result_files: array of string

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: number

    The seed used for the fine-tuning job.

  • status: "validating_files" or "queued" or "running" or 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: number

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: string

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: string

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: optional number

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: optional array of FineTuningJobWandbIntegrationObject

    A list of integrations to enable for this fine-tuning job.

    • type: "wandb"

      The type of the integration being enabled for the fine-tuning job

    • wandb: object { project, entity, name, tags }

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: string

        The name of the project that the new run will be created under.

      • entity: optional string

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: optional string

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: optional array of string

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: optional map[string]

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • method: optional object { type, dpo, reinforcement, supervised }

    The method used for fine-tuning.

    • type: "supervised" or "dpo" or "reinforcement"

      The type of method. Is either supervised, dpo, or reinforcement.

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: optional object { hyperparameters }

      Configuration for the DPO fine-tuning method.

      • hyperparameters: optional object { batch_size, beta, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • beta: optional "auto" or number

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

    • reinforcement: optional object { grader, hyperparameters }

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

        The grader used for the fine-tuning job.

        • string_check_grader: object { input, name, operation, 2 more }

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: string

            The input text. This may include template strings.

          • name: string

            The name of the grader.

          • operation: "eq" or "ne" or "like" or "ilike"

            The string check operation to perform. One of eq, ne, like, or ilike.

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: string

            The reference text. This may include template strings.

          • type: "string_check"

            The object type, which is always string_check.

        • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: string

            The text being graded.

          • name: string

            The name of the grader.

          • reference: string

            The text being graded against.

          • type: "text_similarity"

            The type of grader.

        • python_grader: object { name, source, type, image_tag }

          A PythonGrader object that runs a python script on the input.

          • name: string

            The name of the grader.

          • source: string

            The source code of the python script.

          • type: "python"

            The object type, which is always python.

          • image_tag: optional string

            The image tag to use for the python script.

        • score_model_grader: object { input, model, name, 3 more }

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: array of object { content, role, type }

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: string or ResponseInputText or object { text, type } or 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • Text input: string

                A text input to the model.

              • response_input_text: object { text, type }

                A text input to the model.

                • text: string

                  The text input to the model.

                • type: "input_text"

                  The type of the input item. Always input_text.

              • Output text: object { text, type }

                A text output from the model.

                • text: string

                  The text output from the model.

                • type: "output_text"

                  The type of the output text. Always output_text.

              • Input image: object { image_url, type, detail }

                An image input block used within EvalItem content arrays.

                • image_url: string

                  The URL of the image input.

                • type: "input_image"

                  The type of the image input. Always input_image.

                • detail: optional string

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • response_input_audio: object { input_audio, type }

                An audio input to the model.

                • input_audio: object { data, format }

                  • data: string

                    Base64-encoded audio data.

                  • format: "mp3" or "wav"

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • "mp3"

                    • "wav"

                • type: "input_audio"

                  The type of the input item. Always input_audio.

              • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • Text input: string

                  A text input to the model.

                • response_input_text: object { text, type }

                  A text input to the model.

                • Output text: object { text, type }

                  A text output from the model.

                  • text: string

                    The text output from the model.

                  • type: "output_text"

                    The type of the output text. Always output_text.

                • Input image: object { image_url, type, detail }

                  An image input block used within EvalItem content arrays.

                  • image_url: string

                    The URL of the image input.

                  • type: "input_image"

                    The type of the image input. Always input_image.

                  • detail: optional string

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • response_input_audio: object { input_audio, type }

                  An audio input to the model.

            • role: "user" or "assistant" or "system" or "developer"

              The role of the message input. One of user, assistant, system, or developer.

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: optional "message"

              The type of the message input. Always message.

              • "message"

          • model: string

            The model to use for the evaluation.

          • name: string

            The name of the grader.

          • type: "score_model"

            The object type, which is always score_model.

          • range: optional array of number

            The range of the score. Defaults to [0, 1].

          • sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }

            The sampling parameters for the model.

            • max_completions_tokens: optional number

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: optional "none" or "minimal" or "low" or 3 more

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

              • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.

              • All models before gpt-5.1 default to medium reasoning effort, and do not support none.

              • The gpt-5-pro model defaults to (and only supports) high reasoning effort.

              • xhigh is supported for all models after gpt-5.1-codex-max.

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

            • seed: optional number

              A seed value to initialize the randomness, during sampling.

            • temperature: optional number

              A higher temperature increases randomness in the outputs.

            • top_p: optional number

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • multi_grader: object { calculate_output, graders, name, type }

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: string

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • string_check_grader: object { input, name, operation, 2 more }

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • python_grader: object { name, source, type, image_tag }

              A PythonGrader object that runs a python script on the input.

            • score_model_grader: object { input, model, name, 3 more }

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • label_model_grader: object { input, labels, model, 3 more }

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: array of object { content, role, type }

                • content: string or ResponseInputText or object { text, type } or 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • Text input: string

                    A text input to the model.

                  • response_input_text: object { text, type }

                    A text input to the model.

                  • Output text: object { text, type }

                    A text output from the model.

                    • text: string

                      The text output from the model.

                    • type: "output_text"

                      The type of the output text. Always output_text.

                  • Input image: object { image_url, type, detail }

                    An image input block used within EvalItem content arrays.

                    • image_url: string

                      The URL of the image input.

                    • type: "input_image"

                      The type of the image input. Always input_image.

                    • detail: optional string

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • response_input_audio: object { input_audio, type }

                    An audio input to the model.

                  • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: "user" or "assistant" or "system" or "developer"

                  The role of the message input. One of user, assistant, system, or developer.

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: optional "message"

                  The type of the message input. Always message.

                  • "message"

              • labels: array of string

                The labels to assign to each item in the evaluation.

              • model: string

                The model to use for the evaluation. Must support structured outputs.

              • name: string

                The name of the grader.

              • passing_labels: array of string

                The labels that indicate a passing result. Must be a subset of labels.

              • type: "label_model"

                The object type, which is always label_model.

          • name: string

            The name of the grader.

          • type: "multi"

            The object type, which is always multi.

      • hyperparameters: optional object { batch_size, compute_multiplier, eval_interval, 4 more }

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • compute_multiplier: optional "auto" or number

          Multiplier on amount of compute used for exploring search space during training.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_interval: optional "auto" or number

          The number of training steps between evaluation runs.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_samples: optional "auto" or number

          Number of evaluation samples to generate per training step.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

        • reasoning_effort: optional "default" or "low" or "medium" or "high"

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: optional object { hyperparameters }

      Configuration for the supervised fine-tuning method.

      • hyperparameters: optional object { batch_size, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

Example

openai fine-tuning:jobs pause \
  --api-key 'My API Key' \
  --fine-tuning-job-id ft-AF1WoRqd3aJAHsqc9NY7iL8F

Response

{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}

Resume fine-tuning

$ openai fine-tuning:jobs resume

post /fine_tuning/jobs/{fine_tuning_job_id}/resume

Resume a fine-tune job.

Parameters

• --fine-tuning-job-id: string

  The ID of the fine-tuning job to resume.

Returns

• fine_tuning_job: object { id, created_at, error, 16 more }

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: string

    The object identifier, which can be referenced in the API endpoints.

  • created_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: object { code, message, param }

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: string

      A machine-readable error code.

    • message: string

      A human-readable error message.

    • param: string

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: string

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: object { batch_size, learning_rate_multiplier, n_epochs }

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: optional "auto" or number

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • union_member_0: "auto"

      • union_member_1: number

    • learning_rate_multiplier: optional "auto" or number

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • Auto: "auto"

      • union_member_1: number

    • n_epochs: optional "auto" or number

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • Auto: "auto"

      • union_member_1: number

  • model: string

    The base model that is being fine-tuned.

  • object: "fine_tuning.job"

    The object type, which is always "fine_tuning.job".

  • organization_id: string

    The organization that owns the fine-tuning job.

  • result_files: array of string

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: number

    The seed used for the fine-tuning job.

  • status: "validating_files" or "queued" or "running" or 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: number

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: string

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: string

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: optional number

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: optional array of FineTuningJobWandbIntegrationObject

    A list of integrations to enable for this fine-tuning job.

    • type: "wandb"

      The type of the integration being enabled for the fine-tuning job

    • wandb: object { project, entity, name, tags }

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: string

        The name of the project that the new run will be created under.

      • entity: optional string

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: optional string

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: optional array of string

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: optional map[string]

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • method: optional object { type, dpo, reinforcement, supervised }

    The method used for fine-tuning.

    • type: "supervised" or "dpo" or "reinforcement"

      The type of method. Is either supervised, dpo, or reinforcement.

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: optional object { hyperparameters }

      Configuration for the DPO fine-tuning method.

      • hyperparameters: optional object { batch_size, beta, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • beta: optional "auto" or number

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

    • reinforcement: optional object { grader, hyperparameters }

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

        The grader used for the fine-tuning job.

        • string_check_grader: object { input, name, operation, 2 more }

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: string

            The input text. This may include template strings.

          • name: string

            The name of the grader.

          • operation: "eq" or "ne" or "like" or "ilike"

            The string check operation to perform. One of eq, ne, like, or ilike.

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: string

            The reference text. This may include template strings.

          • type: "string_check"

            The object type, which is always string_check.

        • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: string

            The text being graded.

          • name: string

            The name of the grader.

          • reference: string

            The text being graded against.

          • type: "text_similarity"

            The type of grader.

        • python_grader: object { name, source, type, image_tag }

          A PythonGrader object that runs a python script on the input.

          • name: string

            The name of the grader.

          • source: string

            The source code of the python script.

          • type: "python"

            The object type, which is always python.

          • image_tag: optional string

            The image tag to use for the python script.

        • score_model_grader: object { input, model, name, 3 more }

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: array of object { content, role, type }

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: string or ResponseInputText or object { text, type } or 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • Text input: string

                A text input to the model.

              • response_input_text: object { text, type }

                A text input to the model.

                • text: string

                  The text input to the model.

                • type: "input_text"

                  The type of the input item. Always input_text.

              • Output text: object { text, type }

                A text output from the model.

                • text: string

                  The text output from the model.

                • type: "output_text"

                  The type of the output text. Always output_text.

              • Input image: object { image_url, type, detail }

                An image input block used within EvalItem content arrays.

                • image_url: string

                  The URL of the image input.

                • type: "input_image"

                  The type of the image input. Always input_image.

                • detail: optional string

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • response_input_audio: object { input_audio, type }

                An audio input to the model.

                • input_audio: object { data, format }

                  • data: string

                    Base64-encoded audio data.

                  • format: "mp3" or "wav"

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • "mp3"

                    • "wav"

                • type: "input_audio"

                  The type of the input item. Always input_audio.

              • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • Text input: string

                  A text input to the model.

                • response_input_text: object { text, type }

                  A text input to the model.

                • Output text: object { text, type }

                  A text output from the model.

                  • text: string

                    The text output from the model.

                  • type: "output_text"

                    The type of the output text. Always output_text.

                • Input image: object { image_url, type, detail }

                  An image input block used within EvalItem content arrays.

                  • image_url: string

                    The URL of the image input.

                  • type: "input_image"

                    The type of the image input. Always input_image.

                  • detail: optional string

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • response_input_audio: object { input_audio, type }

                  An audio input to the model.

            • role: "user" or "assistant" or "system" or "developer"

              The role of the message input. One of user, assistant, system, or developer.

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: optional "message"

              The type of the message input. Always message.

              • "message"

          • model: string

            The model to use for the evaluation.

          • name: string

            The name of the grader.

          • type: "score_model"

            The object type, which is always score_model.

          • range: optional array of number

            The range of the score. Defaults to [0, 1].

          • sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }

            The sampling parameters for the model.

            • max_completions_tokens: optional number

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: optional "none" or "minimal" or "low" or 3 more

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

              • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.

              • All models before gpt-5.1 default to medium reasoning effort, and do not support none.

              • The gpt-5-pro model defaults to (and only supports) high reasoning effort.

              • xhigh is supported for all models after gpt-5.1-codex-max.

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

            • seed: optional number

              A seed value to initialize the randomness, during sampling.

            • temperature: optional number

              A higher temperature increases randomness in the outputs.

            • top_p: optional number

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • multi_grader: object { calculate_output, graders, name, type }

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: string

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • string_check_grader: object { input, name, operation, 2 more }

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • python_grader: object { name, source, type, image_tag }

              A PythonGrader object that runs a python script on the input.

            • score_model_grader: object { input, model, name, 3 more }

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • label_model_grader: object { input, labels, model, 3 more }

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: array of object { content, role, type }

                • content: string or ResponseInputText or object { text, type } or 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • Text input: string

                    A text input to the model.

                  • response_input_text: object { text, type }

                    A text input to the model.

                  • Output text: object { text, type }

                    A text output from the model.

                    • text: string

                      The text output from the model.

                    • type: "output_text"

                      The type of the output text. Always output_text.

                  • Input image: object { image_url, type, detail }

                    An image input block used within EvalItem content arrays.

                    • image_url: string

                      The URL of the image input.

                    • type: "input_image"

                      The type of the image input. Always input_image.

                    • detail: optional string

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • response_input_audio: object { input_audio, type }

                    An audio input to the model.

                  • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: "user" or "assistant" or "system" or "developer"

                  The role of the message input. One of user, assistant, system, or developer.

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: optional "message"

                  The type of the message input. Always message.

                  • "message"

              • labels: array of string

                The labels to assign to each item in the evaluation.

              • model: string

                The model to use for the evaluation. Must support structured outputs.

              • name: string

                The name of the grader.

              • passing_labels: array of string

                The labels that indicate a passing result. Must be a subset of labels.

              • type: "label_model"

                The object type, which is always label_model.

          • name: string

            The name of the grader.

          • type: "multi"

            The object type, which is always multi.

      • hyperparameters: optional object { batch_size, compute_multiplier, eval_interval, 4 more }

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • compute_multiplier: optional "auto" or number

          Multiplier on amount of compute used for exploring search space during training.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_interval: optional "auto" or number

          The number of training steps between evaluation runs.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_samples: optional "auto" or number

          Number of evaluation samples to generate per training step.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

        • reasoning_effort: optional "default" or "low" or "medium" or "high"

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: optional object { hyperparameters }

      Configuration for the supervised fine-tuning method.

      • hyperparameters: optional object { batch_size, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

Example

openai fine-tuning:jobs resume \
  --api-key 'My API Key' \
  --fine-tuning-job-id ft-AF1WoRqd3aJAHsqc9NY7iL8F

Response

{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}

Domain Types

Fine Tuning Job

• fine_tuning_job: object { id, created_at, error, 16 more }

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: string

    The object identifier, which can be referenced in the API endpoints.

  • created_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: object { code, message, param }

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: string

      A machine-readable error code.

    • message: string

      A human-readable error message.

    • param: string

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: string

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: number

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: object { batch_size, learning_rate_multiplier, n_epochs }

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: optional "auto" or number

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • union_member_0: "auto"

      • union_member_1: number

    • learning_rate_multiplier: optional "auto" or number

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • Auto: "auto"

      • union_member_1: number

    • n_epochs: optional "auto" or number

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • Auto: "auto"

      • union_member_1: number

  • model: string

    The base model that is being fine-tuned.

  • object: "fine_tuning.job"

    The object type, which is always "fine_tuning.job".

  • organization_id: string

    The organization that owns the fine-tuning job.

  • result_files: array of string

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: number

    The seed used for the fine-tuning job.

  • status: "validating_files" or "queued" or "running" or 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: number

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: string

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: string

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: optional number

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: optional array of FineTuningJobWandbIntegrationObject

    A list of integrations to enable for this fine-tuning job.

    • type: "wandb"

      The type of the integration being enabled for the fine-tuning job

    • wandb: object { project, entity, name, tags }

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: string

        The name of the project that the new run will be created under.

      • entity: optional string

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: optional string

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: optional array of string

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: optional map[string]

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • method: optional object { type, dpo, reinforcement, supervised }

    The method used for fine-tuning.

    • type: "supervised" or "dpo" or "reinforcement"

      The type of method. Is either supervised, dpo, or reinforcement.

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: optional object { hyperparameters }

      Configuration for the DPO fine-tuning method.

      • hyperparameters: optional object { batch_size, beta, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • beta: optional "auto" or number

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

    • reinforcement: optional object { grader, hyperparameters }

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

        The grader used for the fine-tuning job.

        • string_check_grader: object { input, name, operation, 2 more }

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: string

            The input text. This may include template strings.

          • name: string

            The name of the grader.

          • operation: "eq" or "ne" or "like" or "ilike"

            The string check operation to perform. One of eq, ne, like, or ilike.

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: string

            The reference text. This may include template strings.

          • type: "string_check"

            The object type, which is always string_check.

        • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: string

            The text being graded.

          • name: string

            The name of the grader.

          • reference: string

            The text being graded against.

          • type: "text_similarity"

            The type of grader.

        • python_grader: object { name, source, type, image_tag }

          A PythonGrader object that runs a python script on the input.

          • name: string

            The name of the grader.

          • source: string

            The source code of the python script.

          • type: "python"

            The object type, which is always python.

          • image_tag: optional string

            The image tag to use for the python script.

        • score_model_grader: object { input, model, name, 3 more }

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: array of object { content, role, type }

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: string or ResponseInputText or object { text, type } or 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • Text input: string

                A text input to the model.

              • response_input_text: object { text, type }

                A text input to the model.

                • text: string

                  The text input to the model.

                • type: "input_text"

                  The type of the input item. Always input_text.

              • Output text: object { text, type }

                A text output from the model.

                • text: string

                  The text output from the model.

                • type: "output_text"

                  The type of the output text. Always output_text.

              • Input image: object { image_url, type, detail }

                An image input block used within EvalItem content arrays.

                • image_url: string

                  The URL of the image input.

                • type: "input_image"

                  The type of the image input. Always input_image.

                • detail: optional string

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • response_input_audio: object { input_audio, type }

                An audio input to the model.

                • input_audio: object { data, format }

                  • data: string

                    Base64-encoded audio data.

                  • format: "mp3" or "wav"

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • "mp3"

                    • "wav"

                • type: "input_audio"

                  The type of the input item. Always input_audio.

              • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • Text input: string

                  A text input to the model.

                • response_input_text: object { text, type }

                  A text input to the model.

                • Output text: object { text, type }

                  A text output from the model.

                  • text: string

                    The text output from the model.

                  • type: "output_text"

                    The type of the output text. Always output_text.

                • Input image: object { image_url, type, detail }

                  An image input block used within EvalItem content arrays.

                  • image_url: string

                    The URL of the image input.

                  • type: "input_image"

                    The type of the image input. Always input_image.

                  • detail: optional string

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • response_input_audio: object { input_audio, type }

                  An audio input to the model.

            • role: "user" or "assistant" or "system" or "developer"

              The role of the message input. One of user, assistant, system, or developer.

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: optional "message"

              The type of the message input. Always message.

              • "message"

          • model: string

            The model to use for the evaluation.

          • name: string

            The name of the grader.

          • type: "score_model"

            The object type, which is always score_model.

          • range: optional array of number

            The range of the score. Defaults to [0, 1].

          • sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }

            The sampling parameters for the model.

            • max_completions_tokens: optional number

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: optional "none" or "minimal" or "low" or 3 more

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

              • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.

              • All models before gpt-5.1 default to medium reasoning effort, and do not support none.

              • The gpt-5-pro model defaults to (and only supports) high reasoning effort.

              • xhigh is supported for all models after gpt-5.1-codex-max.

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

            • seed: optional number

              A seed value to initialize the randomness, during sampling.

            • temperature: optional number

              A higher temperature increases randomness in the outputs.

            • top_p: optional number

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • multi_grader: object { calculate_output, graders, name, type }

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: string

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • string_check_grader: object { input, name, operation, 2 more }

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • text_similarity_grader: object { evaluation_metric, input, name, 2 more }

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • python_grader: object { name, source, type, image_tag }

              A PythonGrader object that runs a python script on the input.

            • score_model_grader: object { input, model, name, 3 more }

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • label_model_grader: object { input, labels, model, 3 more }

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: array of object { content, role, type }

                • content: string or ResponseInputText or object { text, type } or 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • Text input: string

                    A text input to the model.

                  • response_input_text: object { text, type }

                    A text input to the model.

                  • Output text: object { text, type }

                    A text output from the model.

                    • text: string

                      The text output from the model.

                    • type: "output_text"

                      The type of the output text. Always output_text.

                  • Input image: object { image_url, type, detail }

                    An image input block used within EvalItem content arrays.

                    • image_url: string

                      The URL of the image input.

                    • type: "input_image"

                      The type of the image input. Always input_image.

                    • detail: optional string

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • response_input_audio: object { input_audio, type }

                    An audio input to the model.

                  • grader_inputs: array of string or ResponseInputText or object { text, type } or 2 more

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: "user" or "assistant" or "system" or "developer"

                  The role of the message input. One of user, assistant, system, or developer.

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: optional "message"

                  The type of the message input. Always message.

                  • "message"

              • labels: array of string

                The labels to assign to each item in the evaluation.

              • model: string

                The model to use for the evaluation. Must support structured outputs.

              • name: string

                The name of the grader.

              • passing_labels: array of string

                The labels that indicate a passing result. Must be a subset of labels.

              • type: "label_model"

                The object type, which is always label_model.

          • name: string

            The name of the grader.

          • type: "multi"

            The object type, which is always multi.

      • hyperparameters: optional object { batch_size, compute_multiplier, eval_interval, 4 more }

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • compute_multiplier: optional "auto" or number

          Multiplier on amount of compute used for exploring search space during training.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_interval: optional "auto" or number

          The number of training steps between evaluation runs.

          • union_member_0: "auto"

          • union_member_1: number

        • eval_samples: optional "auto" or number

          Number of evaluation samples to generate per training step.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number

        • reasoning_effort: optional "default" or "low" or "medium" or "high"

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: optional object { hyperparameters }

      Configuration for the supervised fine-tuning method.

      • hyperparameters: optional object { batch_size, learning_rate_multiplier, n_epochs }

        The hyperparameters used for the fine-tuning job.

        • batch_size: optional "auto" or number

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • union_member_0: "auto"

          • union_member_1: number

        • learning_rate_multiplier: optional "auto" or number

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • union_member_0: "auto"

          • union_member_1: number

        • n_epochs: optional "auto" or number

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • union_member_0: "auto"

          • union_member_1: number